Introduction

Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ), and systems which optimize MapReduce to improve performance on analytical workloads (Shark, Stinger/Tez).

In order to provide an environment for comparing these systems, we draw workloads and queries from "A Comparison of Approaches to Large-Scale Data Analysis" by Pavlo et al. (SIGMOD 2009). The software we provide here is an implementation of these workloads that is entirely hosted on EC2 and can be reproduced from your computer. Please note that results obtained with this software are not directly comparable with results in the paper from Pavlo et al. because we use different data sets and have modified one of the queries (see FAQ).

Shark - a Hive-compatible SQL engine which runs on top of the Spark computing framework. (v0.8.1)

Impala - a Hive-compatible* SQL engine with its own MPP-like execution engine. (v1.2.3)

Stinger/Tez - Tez is a next generation Hadoop execution engine currently in development (v0.2.0)

This remains a work in progress and will evolve to include additional frameworks and new capabilities. We welcome contributions.

What this benchmark is not

This benchmark is not intended to provide a comprehensive overview of the tested platforms. We are aware that by choosing default configurations we have excluded many optimizations. The choice of a simple storage format, compressed SequenceFile, omits optimizations included in columnar formats such as ORCFile and Parquet. For now, we've targeted a simple comparison between these systems with the goal that the results are understandable and reproducible.

What is being evaluated?

This benchmark measures response time on a handful of relational queries: scans, aggregations, joins, and UDF's, across different data sizes. Keep in mind that these systems have very different sets of capabilities. MapReduce-like systems (Shark/Hive) target flexible and large-scale computation, supporting complex User Defined Functions (UDF's), tolerating failures, and scaling to thousands of nodes. Traditional MPP databases are strictly SQL compliant and heavily optimized for relational queries. The workload here is simply one set of queries that most of these systems these can complete.

Changes and Notes (February 2014)

We changed the Hive configuration from Hive 0.10 on CDH4 to Hive 0.12 on HDP 2.0.6. As a result, direct comparisons between the current and previous Hive results should not be made. It is difficult to account for changes resulting from modifications to Hive as opposed to changes in the underlying Hadoop distribution.

We have added Tez as a supported platform. It is important to note that Tez is currently in a preview state.

Hive has improved its query optimization, which is also inherited by Shark. This set of queries does not test the improved optimizer.

We have changed the underlying filesystem from Ext3 to Ext4 for Hive, Tez, Impala, and Shark benchmarking.

Dataset and Workload

This work builds on the benchmark developed by Pavlo et al.. In particular, it uses the schema and queries from that benchmark. However, results obtained with this software are not directly comparable with results in the Pavlo et al paper, because we use different data sets, a different data generator, and have modified one of the queries (query 4 below)

Our dataset and queries are inspired by the benchmark contained in a comparison of approaches to large scale analytics. The input data set consists of a set of unstructured HTML documents and two SQL tables which contain summary information. It was generated using Intel's Hadoop benchmark tools and data sampled from the Common Crawl document corpus. There are three datasets with the following schemas:

Query 1 and Query 2 are exploratory SQL queries. We vary the size of the result to expose scaling properties of each systems.

Variant A: BI-Like - result sets are small (e.g., could fit in memory in a BI tool)

Variant B: Intermediate - result set may not fit in memory on one node

Variant C: ETL-Like - result sets are large and require several nodes to store

Query 3 is a join query with a small result set, but varying sizes of joins.

Query 4 is a bulk UDF query. It calculates a simplified version of PageRank using a sample of the Common Crawl dataset.

Hardware Configuration

For Impala, Hive, Tez, and Shark, this benchmark uses the m2.4xlarge EC2 instance type. Redshift only has very small and very large instances, so rather than compare identical hardware, we fix the cost of the cluster and opt to purchase a larger number of small nodes for Redshift. We use a scale factor of 5 for the experiments in all cases.

Instance stats

Framework

Instance Type

Memory

Storage

Virtual Cores

$/hour

Impala, Hive, Tez, Shark

m2.4xlarge

68.4 GB

1680GB (2HDD)

8

1.640

Redshift

dw.hs1.xlarge

15 GB

2 TB (3HDD)

2

.85

Cluster stats

Framework

Instance Type

Instances

Memory

Storage

Virtual Cores

Cluster $/hour

Impala, Hive, Tez, Shark

m2.4xlarge

5

342 GB

8.4 TB (10HDD)

40

$8.20

Redshift

dw.hs1.xlarge

10

150 GB

20 TB (30HDD)

20

$8.50

Results | February 2014

We launch EC2 clusters and run each query several times. We report the median response time here. Except for Redshift, all data is stored on HDFS in compressed SequenceFile format. Each query is run with seven frameworks:

Redshift

Amazon Redshift with default options.

Shark - disk

Input and output tables are on-disk compressed with gzip. OS buffer cache is cleared before each run.

Impala - disk

Input and output tables are on-disk compressed with snappy. OS buffer cache is cleared before each run.

Input tables are coerced into the OS buffer cache. Output tables are on disk (Impala has no notion of a cached table).

Hive

Hive on HDP 2.0.6 with default options. Input and output tables are on disk compressed with snappy. OS buffer cache is cleared before each run.

Tez

Tez with the configuration parameters specified here. Input and output tables are on disk compressed with snappy. OS buffer cache is cleared before each run.

1. Scan Query

SELECTpageURL,pageRankFROMrankingsWHEREpageRank>X

Query 1A32,888 results

Query 1B3,331,851 results

Query 1C89,974,976 results

This query scans and filters the dataset and stores the results.

This query primarily tests the throughput with which each framework can read and write table data. The best performers are Impala (mem) and Shark (mem) which see excellent throughput by avoiding disk. For on-disk data, Redshift sees the best throughput for two reasons. First, the Redshift clusters have more disks and second, Redshift uses columnar compression which allows it to bypass a field which is not used in the query. Shark and Impala scan at HDFS throughput with fewer disks.

Both Shark and Impala outperform Hive by 3-4X due in part to more efficient task launching and scheduling. As the result sets get larger, Impala becomes bottlenecked on the ability to persist the results back to disk. Nonetheless, since the last iteration of the benchmark Impala has improved its performance in materializing these large result-sets to disk.

Tez sees about a 40% improvement over Hive in these queries. This is in part due to the container pre-warming and reuse, which cuts down on JVM initialization time.

2. Aggregation Query

This query applies string parsing to each input tuple then performs a high-cardinality aggregation.

Redshift's columnar storage provides greater benefit than in Query 1 since several columns of the UserVistits table are un-used. While Shark's in-memory tables are also columnar, it is bottlenecked here on the speed at which it evaluates the SUBSTR expression. Since Impala is reading from the OS buffer cache, it must read and decompress entire rows. Unlike Shark, however, Impala evaluates this expression using very efficient compiled code. These two factors offset each other and Impala and Shark achieve roughly the same raw throughput for in memory tables. For larger result sets, Impala again sees high latency due to the speed of materializing output tables.

3. Join Query

SELECTsourceIP,totalRevenue,avgPageRankFROM(SELECTsourceIP,AVG(pageRank)asavgPageRank,SUM(adRevenue)astotalRevenueFROMRankingsASR,UserVisitsASUVWHERER.pageURL=UV.destURLANDUV.visitDateBETWEENDate(`1980-01-01') AND Date(`X')GROUPBYUV.sourceIP)ORDERBYtotalRevenueDESCLIMIT1

Query 3A485,312 rows

Query 3B53,332,015 rows

Query 3C533,287,121 rows

This query joins a smaller table to a larger table then sorts the results.

When the join is small (3A), all frameworks spend the majority of time scanning the large table and performing date comparisons. For larger joins, the initial scan becomes a less significant fraction of overall response time. For this reason the gap between in-memory and on-disk representations diminishes in query 3C. All frameworks perform partitioned joins to answer this query. CPU (due to hashing join keys) and network IO (due to shuffling data) are the primary bottlenecks. Redshift has an edge in this case because the overall network capacity in the cluster is higher.

This query calls an external Python function which extracts and aggregates URL information from a web crawl dataset. It then aggregates a total count per URL.

Impala and Redshift do not currently support calling this type of UDF, so they are omitted from the result set. Impala UDFs must be written in Java or C++, where as this script is written in Python. The performance advantage of Shark (disk) over Hive in this query is less pronounced than in 1, 2, or 3 because the shuffle and reduce phases take a relatively small amount of time (this query only shuffles a small amount of data) so the task-launch overhead of Hive is less pronounced. Also note that when the data is in-memory, Shark is bottlenecked by the speed at which it can pipe tuples to the Python process rather than memory throughput. This makes the speedup relative to disk around 5X (rather than 10X or more seen in other queries).

Discussion

These numbers compare performance on SQL workloads, but raw performance is just one of many important attributes of an analytic framework. The reason why systems like Hive, Impala, and Shark are used is because they offer a high degree of flexibility, both in terms of the underlying format of the data and the type of computation employed. Below we summarize a few qualitative points of comparison:

System

SQL variant

Execution engine

UDF Support

Mid-query fault tolerance

Open source

Commercial support

HDFS Compatible

Hive

Hive QL (HQL)

MapReduce

Yes

Yes

Yes

Yes

Yes

Tez

Hive QL (HQL)

Tez

Yes

Yes

Yes

Yes

Yes

Shark

Hive QL (HQL)

Spark

Yes

Yes

Yes

Yes

Yes

Impala

Some HQL + some extensions

DBMS

Yes (Java/C++)

No

Yes

Yes

Yes

Redshift

Full SQL 92 (?)

DBMS

No

No

No

Yes

No

FAQ

What's next?

We would like to include the columnar storage formats for Hadoop-based systems, such as Parquet and RC file. We would also like to run the suite at higher scale factors, using different types of nodes, and/or inducing failures during execution. Finally, we plan to re-evaluate on a regular basis as new versions are released.

We wanted to begin with a relatively well known workload, so we chose a variant of the Pavlo benchmark. This benchmark is heavily influenced by relational queries (SQL) and leaves out other types of analytics, such as machine learning and graph processing. The largest table also has fewer columns than in many modern RDBMS warehouses. In future iterations of this benchmark, we may extend the workload to address these gaps.

How is this different from the SIGMOD 2009 Pavlo et al. benchmark?

This benchmark is not an attempt to exactly recreate the environment of the Pavlo at al. benchmark. The most notable differences are as follows:

We run on a public cloud instead of using dedicated hardware.

We require the results are materialized to an output table. This is necessary because some queries in our version have results which do not fit in memory on one machine.

The dataset used for Query 4 is an actual web crawl rather than a synthetic one.

Query 4 uses a Python UDF instead of SQL/Java UDF's.

We create different permutations of queries 1-3. These permutations result in shorter or longer response times.

The dataset is generated using the newer Intel generator instead of the original C scripts. The newer tools are well supported and designed to output Hadoop datasets.

We've started with a small number of EC2-hosted query engines because our primary goal is producing verifiable results. Over time we'd like to grow the set of frameworks. We actively welcome contributions!

This workload doesn't represent queries I run -- how can I test these frameworks on my own workload?

We've tried to cover a set of fundamental operations in this benchmark, but of course, it may not correspond to your own workload. The prepare scripts provided with this benchmark will load sample data sets into each framework. From there, you are welcome to run your own types of queries against these tables. Because these are all easy to launch on EC2, you can also load your own datasets.

Do these queries take advantage of different Hadoop file formats or data-layout options, such as Hive/Impala/Shark partitions or Redshift sort columns?

For now, no. The idea is to test "out of the box" performance on these queries even if you haven't done a bunch of up-front work at the loading stage to optimize for specific access patterns. For this reason we have opted to use simple storage formats across Hive, Impala and Shark benchmarking.

That being said, it is important to note that the various platforms optimize different use cases. As it stands, only Redshift can take advantage of its columnar compression. However, the other platforms could see improved performance by utilizing a columnar storage format. Specifically, Impala is likely to benefit from the usage of the Parquet columnar file format.

We may relax these requirements in the future.

Why didn't you test Hive in memory?

We did, but the results were very hard to stabilize. The reason is that it is hard to coerce the entire input into the buffer cache because of the way Hive uses HDFS: Each file in HDFS has three replicas and Hive's underlying scheduler may choose to launch a task at any replica on a given run. As a result, you would need 3X the amount of buffer cache (which exceeds the capacity in these clusters) and or need to have precise control over which node runs a given task (which is not offered by the MapReduce scheduler).

Contributing a New Framework

We plan to run this benchmark regularly and may introduce additional workloads over time. We welcome the addition of new frameworks as well. The only requirement is that running the benchmark be reproducible and verifiable in similar fashion to those already included. The best place to start is by contacting Patrick Wendell from the U.C. Berkeley AMPLab.

Run This Benchmark Yourself

Since Redshift, Shark, Hive, and Impala all provide tools to easily provision a cluster on EC2, this benchmark can be easily replicated.

Hosted data sets

To allow this benchmark to be easily reproduced, we've prepared various sizes of the input dataset in S3. The scale factor is defined such that each node in a cluster of the given size will hold ~25GB of the UserVisits table, ~1GB of the Rankings table, and ~30GB of the web crawl, uncompressed. The datasets are encoded in TextFile and SequenceFile format along with corresponding compressed versions. They are available publicly at s3n://big-data-benchmark/pavlo/[text|text-deflate|sequence|sequence-snappy]/[suffix].

S3 Suffix

Scale Factor

Rankings (rows)

Rankings (bytes)

UserVisits (rows)

UserVisits (bytes)

Documents (bytes)

/tiny/

small

1200

77.6KB

10000

1.7MB

6.8MB

/1node/

1

18 Million

1.28GB

155 Million

25.4GB

29.0GB

/5nodes/

5

90 Million

6.38GB

775 Million

126.8GB

136.9GB

Launching and Loading Clusters

Create an Impala, Redshift, Hive/Tez or Shark cluster using their provided provisioning tools.

Each cluster should be created in the US East EC2 Region

For Redshift, use the Amazon AWS console. Make sure to whitelist the node you plan to run the benchmark from in the Redshift control panel.

If you are adding a new framework or using this to produce your own scientific performance numbers, get in touch with us. The virtualized environment of EC2 makes eeking out the best results a bit tricky. We can help.